DOCSLIB.ORG

Development of Assamese Wordnet

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Development of Assamese Wordnet Development of Assamese WordNet Iftikar Hussain, Navanath Saharia and Utpal Sharma Department of Computer Science & Engineering Tezpur University, Napaam, India-784028 [email protected], [email protected], [email protected] Abstract. An important challenge in the task of semantic analysis is the fact that in natural languages several words may denote the same concept, and a single word form may denote different concepts. WordNet is a repository of information about such characteristics of words, in a readily accessible format. A WordNet may be developed for a language for which computational processing is attempted. It calls for efforts in the domain of linguistics as well as computer science. In this paper we describe our works towards the development of WordNet for Assamese language, an official language of India. We cover proposed formats for the WordNet database text files and also an Application Programming Interface (API) of the Assamese WordNet. 1 Introduction One of the main challenges in Natural Language Processing (NLP) is determining the appropriate sense of each word that occurs in input expressions. Words in natural languages often have multiple senses, and often several distinct words denote the same sense. WordNet helps to overcome such challenges. WordNet is a database that consists of words or collocations. Words having similar senses are grouped together and the groups are interconnected through some lexical and semantic relations. Users or their applications can make queries on it and can find out appropriate senses of words distinctly. Assamese is one of the 22 official languages in India, spoken by nearly 30 million people. WordNets are being built for about thirteen of these official languages at different institutions. Hindi WordNet, developed at IIT Bombay is the first WordNet developed for an Indian language. Assamese is a morphologically rich, free word order Indic language, where very little computational work is reported, viz. [1, 2, ?,?,?]. Our work reported in this paper is one of the first efforts towards building an Assamese WordNet. Though work on building Assamese WordNet has been taken up in Gauhati University[3] recently, results thereof are still awaited. The proposed architecture of our Assamese WordNet comprises a database and a graphical user interface (GUI). The WordNet database consists of text files which include the synonym set (synset) of a word and the sense of the word. Synsets are interconnected with other synsets via a number of lexical and semantic relations. The database consist of three text files namely index file, data file and ontology file. Assamese linguistic data are maintained in the database files in some pre-defined format. An Application Programming Interface (API) is developed through which other applications can access the WordNet database for their purposes. In course of our work, we had to deal with several issues related to the support of Assamese script in the computer, such as encoding, text typing etc. In the next section we describe the basics of WordNet. Section 3 explores some existing work on WordNet and section 4 discusses issues faced during implementation. In section 5 we discuss our proposed architecture for Assamese WordNet and current status. Section 6 concludes this paper indicating future directions. 2 WordNet WordNet is a repository of words of a language. The words are grouped together according to their similarity of meanings. For each word there is a synonym set called synset, representing one lexical relation. For each synset there is another element called gloss that describes the concept. Synsets in the WordNet are connected to other synsets via a number of lexical and semantic relations. Each entry of the wordNet1 consists of following elements 1. Synset : Words in a synset are arranged according to the frequency of usage. Synset: f emO, mxu, mkrÅg; TF2: fmou, modhu, makarandag; EM3: Honey. 2. Gloss : It describes the concepts. It consists of two parts (a) Text Definition:It explains concepts denoted by the synset. Example- fulr imZA rs; TF: phulor mithA ras; EM: Flower's honey. (b) Example Sentence:It gives the usage of the words in the sentence. Example- emO mAiKey fulr prA emO egATA¡; TF: mou mAkhiye phulor porA mou gotAi ; EM: Honey bee collects honey from flowers.; 3. Position in Ontology : An ontology is a hierarchical organization of concepts, more specifically, a categorization of entities and actions. For each syntactic category namely noun, verb, adjective and adverb, a separate 1 Hindi WordNet Data and Associated Software License Agreement. The IIT-Bombay represented for the purpose of the signature of this agreement by the Dean of Research and Development, IIT Bombay or by his authorized representative Dr. Pushpak Bhattacharyya, Professor of the Department of Computer Science and Engineering, IIT Bombay. 2 TF: Transliterated Assamese Form. 3 EM: English Meaning (Concept). ontological hierarchy is present. Each synset is mapped into some place in the ontology. A synset may have multiple parents. The ontology for the synset representing the concept school is shown in figure 1. Example- Synset = f øul, ibdAly, pAZSAlA g; TF: fskul, bidyAlay, pAthchAlAg; EM: School Fig. 1. Ontology for Synset for \School " 3 Related Work The Princeton English WordNet4 was developed by Professor G. A. Miller at the Cognitive Sciences Laboratory at Princeton University. A variety of lexical and semantic relations are used to represent the organization of lexical knowledge. Inputs provided through text files written by lexicographers are converted to database files. Two kinds of building blocks are distinguished in the source files: word forms and word meanings. There are separate files corresponding to each syntactic category such as noun, verb, adjective and adverb. All of the synsets in a lexicographer file are in the same syntactic category. For each syntactic category, two files are needed to represent the contents of the WordNet database - index.pos and data.pos, where pos is noun, verb, adj and adv. Each index file is an alphabetized list of all the words found in WordNet in the corresponding part 4 http://wordnet.princeton.edu of speech. A data file for a syntactic category contains information corresponding to the synsets that were specified in the lexicographer files. The creation of WordNet is a time consuming and manpower intensive exercise. The effort can be reduced to some extent by using text repositories such as the web and certain corpora, and also by translating an existing WordNet into another language. But results of such attempt are often far from ideal, in the sense that the WordNet so produced contains synsets that have outlier words and/or missing words. Additionally, semantic relations may be inappropriately set up or may be missing altogether. [4] reported an automatic method of WordNet evaluation for the first time. They focused on verifying synonymy within non-singleton synsets and also on hypernymy between synsets. They made some rule based algorithm to validate the synonyms and hypernyms. The synonym validation was tested on the Princeton WordNet (v2.1) noun synsets. Out of the 81426 noun synsets, 39840 are synsets with more than one word, and only these were given as input to the validator. The result gave 70% accuracy where all words in synsets were validated, approximately 90% where half were validated and about 9% where no words were validated. The Hypernym validation algorithm was able to validate 56203 out of 79297 noun hypernymy relation pairs in the Princeton WordNet, giving a validation percentage of 70.88%. The validation algorithm is available only for Princeton WordNet. However, the approach should broadly be applicable to other language WordNets as well. Hindi WordNet is the first WordNet developed for an Indian language. It is developed at CFILT, IIT Bombay. Among other Indian languages WordNets for, Marathi, Bengali, Nepali, Oriya, Telugu, Malayalam, Konkani, Kashmiri, Manipuri etc. are being developed at different Indian institutions. The NE WordNet[3] covering Assamese and Bodo languages is being developed at Gauhati University. 4 Assamese WordNet Assamese WordNet is a database of Assamese word forms (words and collocations) which are grouped together in the form of synsets. The synsets are interconnected to other synsets via a number of lexical and semantic relations such as hypernym and hyponym (the is-a relation), meronym and holonym (the part-of relation), antonyms etc. The lexical relationships hold between semantically related forms of words and the semantic relationships hold between related word definitions. The subgraph of a WordNet holding different relationships are shown in figure 2 Relations between the synsets in the Assamese WordNet are described below. { Hyponym and hypernym (is a kind of): Hypernymy is a semantic relation between two synsets to capture super-set hood. Similarly, hyponymy is a semantic relation between two synsets to capture sub-set hood. Example: f egÆA¡ ful, nAij^ ful, egÅA ful g =>hr f ful, puõ, kusum g; TF: f gendhAi phul, nArji phul, gendA phul g =>hr fphul, puspa,kusum g; Fig. 2. Subgraph of a WordNet EM: (name of a flower) =>hr (flower). ful (phul) is Hypernym of egÆA¡ ful (gendhai phul). egÆA¡ ful (gendhai phul) is a Hyponym of ful (phul). { Meronym and Holonym (part-whole relation): It is a semantic relation between two synsets. If the concepts A and B are related in such a manner that A is one of the constituent of B, then A is the meronym of B and B is the holonym of A. The meronymy relation is transitive and asymmetrical. Holonymy is the reverse of meronymy. It is used to construct a part-of hierarchy. Example: f ekAZAil, ekAZA, kg =>mh f Gr, bAs-vWn, g h g; TF: fkothAli, kothA, kakkhyag =>mh f ghor, bas-bhawan, grihag; EM: (room) =>mh (house). Here ekAZAil (kothali) is a part of Gr (ghor). Therefore, ekAZAil (kothali) is a Meronym of Gr(ghor) and Gr is a Holonym of ekAZAil.
Load more

Recommended publications
  • An Efficient Database Design for Indowordnet Development Using
    An Efficient Database Design for IndoWordNet Development Using Hybrid Approach Venkatesh Prabhu2 Shilpa Desai1 Hanumant Redkar1 N eha Prabhugaonkar1 Apurva N agvenkar1 Ramdas Karmali1 (1) GOA UNIVERSITY, Taleigao - Goa (2) THYWAY CREATIONS, Mapusa - Goa [email protected], [email protected], [email protected], [email protected], [email protected], [email protected] ABSTRACT WordNet is a crucial resource that aids in Natural Language Processing (NLP) tasks such as Machine Translation, Information Retrieval, Word Sense Disambiguation, Multi-lingual Dictionary creation, etc. The IndoWordNet is a multilingual WordNet which links WordNets of different Indian languages on a common identification number given to each concept. WordNet is designed to capture the vocabulary of a language and can be considered as a dictionary cum thesaurus and much more. WordNets for some Indian Languages are being developed using expansion approach. In this paper we have discussed the details and our experiences during the evolution of this database design while working on the Indradhanush WordNet Project. The Indradhanush WordNet Project is working on the development of WordNets for seven Indian languages. Our database design gives an efficient plan for storage of WordNet data for all languages. In addition it extends the design to hold specific concepts for a language. KEYWORDS: WordNet, IndoWordNet, synset, database design, expansion approach, semantic relation, lexical relation. Proceedings of the 3rd Workshop on South and Southeast Asian Natural Language Processing (SANLP), pages 229–236, COLING 2012, Mumbai, December 2012. 229 1 Introduction 1.1 WordNet and its storage methods WordNet (Miller, 1993) maintains the concepts in a language, relations between concepts and their ontological details.
    [Show full text]
  • Exploring Resources in Word Sense Disambiguation for Marathi Language Amit Patil1, Chhaya Patil2, Dr
    www.rspsciencehub.com Volume 02 Issue 10S October 2020 Special Issue of First International Conference on Advancements in Management, Engineering and Technology (ICAMET 2020) Exploring Resources in Word Sense Disambiguation for Marathi Language Amit Patil1, Chhaya Patil2, Dr. Rakesh Ramteke3, Dr. R. P. Bhavsar4, Dr. Hemant Darbari5 1,2 Assistant Professor, Department of Computer Application, RCPET’s IMRD, Maharashtra, India 3,4Professor, School of Computer Sciences, KBC North Maharashtra University, Maharashtra, India 5Director General, Centre for Development of Advanced Computing (C-DAC), Maharashtra, India Abstract Word Sense Disambiguation (WSD) is one of the most challenging problems in the research area of natural language processing. To find the correct sense of the word in a particular context is called Word Sense Disambiguation. As a human, we can get a correct sense of the word given in the sentence because of word knowledge of that particular natural language, but it is not an easy task for the machine to disambiguate the word. Developing any WSD system, it required sense repository and sense dictionary. It is very costly and time-consuming to build these resources. Many foreign languages have available these resources, that is why most of the foreign languages like English, German, Spanish etc lot of work is done in these Natural languages. When we look for Indian languages like Hindi, Marathi, Bengali etc. very less work is done. The reason behind this is resource-scarcity. In this paper, we majorly focus on Marathi Language Word Sense Disambiguation because of very less work is done in the Marathi Language as compared to Hindi and other Indian Languages.
    [Show full text]
  • WWDS Apis: Application Programming Interfaces for Efficient Manipulation of World Wordnet Database Structure
    Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) WWDS APIs: Application Programming Interfaces for Efficient Manipulation of World WordNet Database Structure Hanumant Redkar1, Sudha Bhingardive1, Kevin Patel1, Pushpak Bhattacharyya1 Neha Prabhugaonkar2, Apurva Nagvenkar2, Ramdas Karmali2 1Indian Institute of Technology Bombay, Mumbai, India 2Goa University, Goa, India {hanumantredkar, bhingardivesudha, kevin.svnit, pushpakbh}@gmail.com {nehapgaonkar.1920, apurv.nagvenkar, ramdas.karmali}@gmail.com Abstract example, developers can potentially extract information WordNets are useful resources for natural language from other WordNets through WWDS and its APIs that is processing. Various WordNets for different languages have missing in their source WordNet. The WWDS and WWDS been developed by different groups. Recently, World APIs are explained in the following sections. WordNet Database Structure (WWDS) was proposed by Redkar et. al (2015) as a common platformm to store these different WordNets. However, it is underutilized due to lack World WordNet Database Structure of programming interface. In this paper, we present WWDS APIs, which are designed to address this shortcoming. These WWDS is an efficient storage mechanism which uses WWDS APIs, in conjunction with WWDS, act as a wrapper that enables developers to utilize WordNets without multiple databases to accommodate different WordNets. Its worrying about the underlying storage structure. The APIs design is based on IndoWordNet database structure are developed in PHP, Java, and Python, as they are the (Prabhu et al., 2012). The language independent preferred programming languages of most developers and information such as semantic relations, ontology details, researchers working in language technologies. These APIs etc. is stored in a single master database named can help in various applications like machine translation, word sense disambiguation, multilingual information wordnet_master.
    [Show full text]
  • Comparative Study on Currently Available Wordnets
    International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 10 (2018) pp. 8140-8145 © Research India Publications. http://www.ripublication.com Comparative Study on Currently Available WordNets Sreedhi Deleep Kumar Reshma E U PG Scholar PG Scholar Department of Computer Science and Engineering Department of Computer Science and Engineering Vidya Academy of Science and Technology Vidya Academy of Science and Technology Thrissur, India. Thrissur, India. Sunitha C Amal Ganesh Associate Professor Assistant Professor Department of Computer Science and Engineering Department of Computer Science and Engineering Vidya Academy of Science and Technology Vidya Academy of Science and Technology Thrissur, India. Thrissur, India. Abstract SinoTibetan, Tibeto-Burman and Austro-Asiatic. The major ones are the Indo-Aryan, spoken by the northern to western WordNet is an information base which is arranged part of India and Dravidian, spoken by southern part of India. hierarchically in any language. Usually, WordNet is The Eighth Schedule of the Indian Constitution lists 22 implemented using indexed file system. Good WordNets languages, which have been referred to as scheduled available in many languages. However, Malayalam is not languages and given recognition, status and official having an efficient WordNet. WordNet differs from the encouragement. dictionaries in their organization. WordNet does not give pronunciation, derivation morphology, etymology, usage notes, A Dictionary can be called as are source dealing with the or pictorial illustrations. WordNet depicts the semantic relation individual words of a language along with its orthography, between word senses more transparently and elegantly. In this pronunciation, usage, synonyms, derivation, history, work, a general comparison of currently browsable WordNets etymology, etc.
    [Show full text]
  • Improving Semantic Similarity with Cross-Lingual Resources: a Study in Bangla—A Low Resourced Language
    informatics Article Improving Semantic Similarity with Cross-Lingual Resources: A Study in Bangla—A Low Resourced Language Rajat Pandit 1,* , Saptarshi Sengupta 2, Sudip Kumar Naskar 3, Niladri Sekhar Dash 4 and Mohini Mohan Sardar 5 1 Department of Computer Science, West Bengal State University, Kolkata 700126, India 2 Department of Computer Science, University of Minnesota Duluth, Duluth, MN 55812, USA; [email protected] 3 Department of Computer Science & Engineering , Jadavpur University, Kolkata 700032, India; [email protected] 4 Linguistic Research Unit, Indian Statistical Institute, Kolkata 700108, India; [email protected] 5 Department of Bengali, West Bengal State University, Kolkata 700126, India; [email protected] * Correspondence: [email protected] Received: 17 February 2019; Accepted: 20 April 2019; Published: 5 May 2019 Abstract: Semantic similarity is a long-standing problem in natural language processing (NLP). It is a topic of great interest as its understanding can provide a look into how human beings comprehend meaning and make associations between words. However, when this problem is looked at from the viewpoint of machine understanding, particularly for under resourced languages, it poses a different problem altogether. In this paper, semantic similarity is explored in Bangla, a less resourced language. For ameliorating the situation in such languages, the most rudimentary method (path-based) and the latest state-of-the-art method (Word2Vec) for semantic similarity calculation were augmented using cross-lingual resources in English and the results obtained are truly astonishing. In the presented paper, two semantic similarity approaches have been explored in Bangla, namely the path-based and distributional model and their cross-lingual counterparts were synthesized in light of the English WordNet and Corpora.
    [Show full text]
  • A Sentiment Analysis of Gujarati Text Using Gujarati Senti Word Net
    International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-9, July 2019 A Sentiment Analysis of Gujarati Text using Gujarati Senti word Net Lata Gohil, Dharmendra Patel Abstract: Sentiment Analysis plays vital role in decision II. RELATED WORK making. For English language intensive research work is done in this area. Very less work is reported in this domain for Indian Sentiment analysis is useful method to understand opinion languages compared to English language. Gujarati language is expressed in text. “It is one of the most active research areas almost unexplored for this task. More data in form of movie in natural language processing and is also widely studied in reviews, product reviews, social media posts etc are available in data mining, web mining, and information retrieval” [7]. regional languages as people like to use their native language on Beginning research work on sentiment analysis was mostly Internet which leads to need of mining these data in order to understand their opinion. Various tools and resources are focused on English language. However increasing developed for English language and few for Indian languages. non-English language content on Internet led demand to work Gujarati is resource poor language for this task. Motive of this towards other languages. There are two approaches namely paper is to develop sentiment lexical resource for Gujarati Lexicon and Machine Learning are widely explored for language which can be used for sentiment analysis of Gujarati sentiment analysis. Large amount of annotated data is the key text. Hindi SentiWordNet (H-SWN) [1] and synonym relations of requirement of Machine learning approach.
    [Show full text]
  • S.ARULMOZI Assistant Professor Mobile: +91-9441330510 Dept
    S.ARULMOZI Assistant Professor Mobile: +91-9441330510 Dept. of Dravidian & Computational Linguistics Residence: +91-8570-278214 Dravidian University, Kuppam 517426, India Email: [email protected] EDUCATION Ph.D., Applied Linguistics, University of Hyderabad, 1999. PGDTS, Translation Studies, University of Hyderabad, 1995 (66%) M.Phil., Applied Linguistics, University of Hyderabad, 1992 (70.8%) M.A., Linguistics, Bharathiar University, 1990 (64.4%) B.Sc., Chemistry, Bharathiar University, 1988. (54.6%) H.Sc., Board of Higher Secondary Education, Tamil Nadu, 1985 (61.75%) S.S.L.C., Board of Secondary Education, Tamil Nadu, 1983 (64.5%) EMPLOYMENT Designation Department University/Institution Period Assistant Professor Department of Dravidian Dravidian University 29 Dec 2005 and Computational onwards Linguistics Guest Faculty Centre for ALTS University of Hyderabad 19 Jan 2005 to 28 Dec 2005 Member Research AU-KBC Research Centre Anna University 29 Dec 2000 to Staff 15 Jan 2005 Project Fellow Department of Linguistics Tamil University 21 Jan 1999 to 2 Jul 2000 Language Assistant- DoE Project Central Institute of Indian 8 Jun 1998 to Tamil Languages 31 Oct 1998 RESEARCH EXPERIENCE Course Topic Research Guide Year University Ph.D. Aspects of Inflectional Prof. Probal Dasgupta 1998 University of Morphophonology: A & Dr. N. Krupanandam Hyderabad Computational Approach M.Phil. Dynamics of Translation in Prof. Probal Dasgupta 1992 University of Reconstructing Sci-Tech Hyderabad Terminologies M.A. Advertisement Headlines Prof. C.Shanmugom 1990 Bharathiar University 1 ACADEMIC OUTREACH S.No. Role Topic Event Place Date 1. Resource Person BIS Tagset for Workshop on Tamil Madurai 03-03-2013 Tamil POS POS Tagging Kamaraj Tagging University 2.
    [Show full text]
  • An Online Interface for Synset Creation with Special Reference to Sanskrit
    Introduction to Synskarta: An Online Interface for Synset Creation with Special Reference to Sanskrit Hanumant Redkar, Jai Paranjape, Nilesh Joshi, Irawati Kulkarni, Malhar Kulkarni, Pushpak Bhattacharyya Center for Indian Language Technology, Indian Institute of Technology Bombay, India. [email protected], [email protected], [email protected], [email protected], [email protected], [email protected] In this paper, we have taken reference of San- Abstract skrit WordNet 3. Sanskrit is an Indo-Aryan lan- guage and is one of the ancient languages. It has WordNet is a large lexical resource express- vast literature and a rich tradition of creating ing distinct concepts in a language. Synset is léxica. The roots of all languages in the Indo Euro- a basic building block of the WordNet. In this pean family in India can be traced to Sanskrit paper, we introduce a web based lexicogra- (Kulkarni et al., 2010). Sanskrit WordNet is con- pher's interface ‘Synskarta’ which is devel- structed using expansion approach where Hindi oped to create synsets from source language to target language with special reference to WordNet is used as a source (Kulkarni et al., Sanskrit WordNet. We focus on introduction 2010). and implementation of Synskarta and how it While developing Sanskrit WordNet, lexicogra- can help to overcome the limitations of the phers create Sanskrit synsets by referring to Hindi existing system. Further, we highlight the fea- synsets and by following the three principles of tures, advantages, limitations and user evalua- synset creation (Bhattacharyya, 2010). Since San- tions of the same. Finally, we mention the skrit came into existence much before Hindi, it has scope and enhancements to the Synskarta and many words which are not present in Hindi its usefulness in the entire IndoWordNet WordNet.
    [Show full text]
  • Gujarati Language: Research Issues, Resources and Proposed Method on Word Sense Disambiguation
    International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-2S11, September 2019 Gujarati Language: Research Issues, Resources and Proposed Method on Word Sense Disambiguation Tarjni Vyas, Amit Ganatra B. GUJARATI GRAMMAR Abstract: Gujarati Word Sense Disambiguation (WSD) is an There are 32 consonants and 8 vowels in Gujarati language. exceptionally complex when it comes to Natural language handling because it needs to manage complexities found in a No Components Number Details language. In this paper, the discussion has put forward about 1 Consonants 32 Fig2 Guajarati language, Gujarati Wordnet and Gujarati word sense disambiguation. Accordingly, the deep learning approach is 2 Vowels 8 Fig2 found to perform better in Gujarati WSD yet one of its weakness is 3 Tenses 3 Past,Present, Future the prerequisite of enormous information sources without which 4 Vachans 2 Singular,Plural preparing is close to impossible. On the other hand, utilizes 5 Sentence Structure 3 Subject,object,Verb information sources to choose the meanings of words in a specific setting. Provided with that, deep learning approaches appear to be Sentence structure is made in the order of Subject, Object and more suitable to manage word sense disambiguation; however, the process will always be challenging given the ambiguity of Verb in Gujarati language. natural languages. There are three Tenses in Gujarati Past Tense, Present Tense Keywords: Word Sense Disambiguation, Gujarati Language, and Future Tense. There are two types of Vachans in Deep learning, Natural language processing, Lesk Gujarati.Singular and Plural. Gujarati Language includes Algorithm, Wordnet. following cases.(Table 1) I.
    [Show full text]
  • Adding a Sense-Annotated Telugu Lexicon
    Enrichment of OntoSenseNet: Adding a Sense-annotated Telugu lexicon Sreekavitha Parupalli and Navjyoti Singh Center for Exact Humanities (CEH) International Institute of Information Technology, Hyderabad, India [email protected] [email protected] Abstract The paper describes the enrichment of OntoSenseNet- a verb- centric lexical resource for Indian Languages. This resource contains a newly developed Telugu-Telugu dictionary. It is important because na- tive speakers can better annotate the senses when both the word and its meaning are in Telugu. Hence efforts are made to develop a soft copy of Telugu dictionary. Our resource also has manually annotated gold standard corpus consisting 8483 verbs, 253 adverbs and 1673 adjectives. Annotations are done by native speakers according to defined annotation guidelines. In this paper, we provide an overview of the annotation proce- dure and present the validation of our resource through inter-annotator agreement. Concepts of sense-class and sense-type are discussed. Addi- tionally, we discuss the potential of lexical sense-annotated corpora in improving word sense disambiguation (WSD) tasks. Telugu WordNet is crowd-sourced for annotation of individual words in synsets and is compared with the developed sense-annotated lexicon (OntoSenseNet) to examine the improvement. Also, we present a special categorization (spatio-temporal classification) of adjectives. 1 Introduction Lexically rich resources form the foundation of all natural language processing (NLP) tasks. Maintaining the quality of resources is thus a high priority issue [5]. Hence, it is important to enhance and maintain the lexical resources of any language. This is of significantly more importance in case of resource poor languages like Telugu [19].
    [Show full text]
  • Indowordnet and Its Linking with Ontology
    IndoWordNet and its Linking with Ontology Brijesh Bhatt Pushpak Bhattacharyya Center For Indian Language Technology, Center For Indian Language Technology, Department of Computer Science and Engineering, Department of Computer Science and Engineering, Indian Institute of Technology, Mumbai Indian Institute of Technology, Mumbai [email protected] [email protected] Abstract hierarchical structure of concepts related by sub- sumption relation, which can be shared between Reasoning about natural language requires applications. Though WordNet is often considered combining semantically rich lexical re- as an ontology (the synset corresponds to concept sources with world knowledge, provided and ‘hypernymy-hyponymy’ relation is similar to by ontologies. In this paper, we describe subsumption), it is a language specific resource linking of WordNets of Indian languages which may vary from language to language. The with an upper ontology SUMO (Sug- ontological issues in WordNet discussed in (pease gested Upper Merged Ontology). This and Fellbaum, 2010; Gangemi et al., 2003; Niles creates multilingual resource for Indian and Pease, 2003) are as follows, languages which can be used in various natural language processing applications. This paper presents the architecture of 1. Confusion between concept and individual: IndoWordNet- Linking of WordNets of WordNet synsets do not distinguish between seventeen different Indian languages and concept and individual. For example, both provides a method to link it with upper on- ‘Martial Art’ and ‘Karate’ are considered as tology SUMO. Two different systems: In- concept. doWordNet navigator and SIGMAKEE in- terface for Indian languages are developed 2. Confusion between object level and meta to access this resource. level concept: WordNet covers both object level and meta level concepts as hyponymy 1 Introduction of same concept.
    [Show full text]
  • Indowordnet Dictionary: an Online Multilingual Dictionary Using Indowordnet
    IndoWordNet Dictionary: An Online Multilingual Dictionary using IndoWordNet Hanumant Redkar Sandhya Singh Center for Indian Language Technology, Center for Indian Language Technology, Indian Institute of Technology Bombay, India Indian Institute of Technology Bombay, India [email protected] [email protected] Nilesh Joshi Anupam Ghosh Center for Indian Language Technology, Center for Indian Language Technology, Indian Institute of Technology Bombay, India Indian Institute of Technology Bombay, India [email protected] [email protected] Pushpak Bhattacharyya Department of Computer Science and Engineering, Indian Institute of Technology Bombay, India [email protected] based, thesaurus based, word usage based and Abstract language based. English WordNet infor- mation is also rendered using this interface. India is a country with diverse culture, lan- The IndoWordNet dictionary will help users guage and varied heritage. Due to this, it is to know meanings of a word in multiple Indi- very rich in languages and their dialects. Be- an languages. ing a multilingual society, a dictionary in multiple languages becomes its need and one of the major resources to support a language. 1 Introduction There are dictionaries for many Indian lan- guages, but very few are available in multiple Language is a constituent element of civilization. languages. WordNet is one of the most prom- In a country like India, diversity is its primary as- inent lexical resources in the field of Natural pect. This leads to varied languages and their dia- Language Processing. IndoWordNet is an in- lects. There are numerous languages in India which tegrated multilingual WordNet for Indian lan- belong to different language families. These lan- guages.
    [Show full text]